Summary Statistics
PlotNParticles <- uds %>%
ggplot(aes(x = tot_nparticles, y = depth, col = profile)) +
facet_wrap(~project) +
geom_point(alpha = 0.3, shape = 1) +
scale_y_reverse() + scale_x_log10()
PlotNParticles

bdsAddTime <- bds %>%
mutate(Hour = hour(time), Day = day(time))
FSG1 <- gam(tot_nparticles~ s(depth, k = 3) + s(Day, k = 3) + s(Hour, k = 4, bs = "cc"), knots = list(Hour = c(0, 24)), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))
FSG2 <- gam(tot_nparticles ~ s(depth, k = 3) + s(Day, k = 3), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))
FSG3 <- gam(tot_nparticles ~ s(depth, k = 3), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))
#FSG4 <- gam(tot_nparticles~ s(depth, k = 3) + s(Hour, k = 4, bs = "cc"), knots = list(Hour = c(0, 24)), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))
summary(FSG1)
Family: gaussian
Link function: identity
Formula:
tot_nparticles ~ s(depth, k = 3) + s(Day, k = 3) + s(Hour, k = 4,
bs = "cc")
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 9.002 0.106 84.96 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(depth) 1.204 1.366 3.346 0.0874 .
s(Day) 1.512 1.761 3.001 0.0386 *
s(Hour) 1.579 2.000 2.713 0.0389 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) = 0.183 Deviance explained = 24.2%
GCV = 0.73885 Scale est. = 0.67365 n = 60
#summary(FSG2)
#summary(FSG3)
#summary(FSG4)
summary(FSG1)$r.sq - summary(FSG2)$r.sq
[1] 0.07590024
summary(FSG2)$r.sq - summary(FSG3)$r.sq
[1] 0.05267319
summary(FSG3)$r.sq
[1] 0.0542546
But there is between projects:
ProjGam <- gam(tot_nparticles~ s(depth, k = 3) + factor(project), knots = list(Hour = c(0, 24)), data = bdsAddTime %>% filter(depth >= 175 & depth <=500))
summary(ProjGam)
Family: gaussian
Link function: identity
Formula:
tot_nparticles ~ s(depth, k = 3) + factor(project)
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.9983 0.4445 20.24 <2e-16 ***
factor(project)P16 17.0001 1.3754 12.36 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(depth) 1 1 3.552 0.064 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) = 0.7 Deviance explained = 70.9%
GCV = 12.411 Scale est. = 11.855 n = 67
PlotPSDmany <- uds %>%
filter(project == "ETNP") %>%
ggplot(aes(x = psd, y = depth, shape = factor(day(time)), fill = hour(time))) +
#geom_path(aes(x = psd_gam)) +
#geom_ribbon(aes(x = psd_gam, xmin = psd_gam - 2 * psd_seg, xmax = psd_gam + 2 * psd_seg), alpha = 0.1, outline_type = "lower") +
geom_point(alpha = .6, size = 2, stroke = 1) +
scale_y_reverse() + scale_shape_manual(values = c(21:25)) +
scale_fill_gradientn(breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) +
labs(x = "Depth (m)", y = "Particle Size Distribution Slope")
PlotParticlesmany <- uds %>%
filter(project == "ETNP") %>%
ggplot(aes(x = tot_nparticles, y = depth, shape = factor(day(time)), fill = hour(time))) +
#geom_path(aes(x = psd_gam)) +
#geom_ribbon(aes(x = psd_gam, xmin = psd_gam - 2 * psd_seg, xmax = psd_gam + 2 * psd_seg), alpha = 0.1, outline_type = "lower") +
geom_point(alpha = .6, size = 2, stroke = 1) +
scale_y_reverse() + scale_shape_manual(values = c(21:25)) +
scale_fill_gradientn(breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) +
scale_x_log10() + theme(legend.position = "none") +
labs(x = "Depth (m)", y = "Particles / L")
PlotFluxmany <- uds %>%
filter(project == "ETNP") %>%
ggplot(aes(x = tot_flux_fit, y = depth, shape = factor(day(time)), fill = hour(time))) +
#geom_path(aes(x = psd_gam)) +
#geom_ribbon(aes(x = psd_gam, xmin = psd_gam - 2 * psd_seg, xmax = psd_gam + 2 * psd_seg), alpha = 0.1, outline_type = "lower") +
geom_point(alpha = .6, size = 2, stroke = 1) +
scale_y_reverse() + scale_shape_manual(values = c(21:25)) +
scale_fill_gradientn(breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) +
scale_x_log10() + theme(legend.position = "none")
plot_grid(
PlotParticlesmany,
PlotPSDmany,
rel_widths = c(2, 3)
)

ggsave("figures/ParticlesPSDMany.png")
Saving 12 x 7.41 in image
ggsave("figures/ParticlesPSDMany.svg")
Saving 12 x 7.41 in image
bdsAddTime <- bds %>%
mutate(Hour = hour(time), Day = day(time))
FSG1 <- gam(psd~ s(depth, k = 3) + s(Day, k = 3) + s(Hour, k = 4, bs = "cc"), knots = list(Hour = c(0, 24)), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))
FSG2 <- gam(psd ~ s(depth, k = 3) + s(Day, k = 3), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))
FSG3 <- gam(psd ~ s(depth, k = 3), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))
FSG4 <- gam(psd~ s(depth, k = 3) + s(Hour, k = 4, bs = "cc"), knots = list(Hour = c(0, 24)), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))
summary(FSG1)
Family: gaussian
Link function: identity
Formula:
psd ~ s(depth, k = 3) + s(Day, k = 3) + s(Hour, k = 4, bs = "cc")
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.96083 0.01988 -199.3 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(depth) 1.713 1.917 56.197 5.5e-15 ***
s(Day) 1.000 1.000 1.078 0.3036
s(Hour) 1.604 2.000 3.049 0.0285 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) = 0.635 Deviance explained = 66.1%
GCV = 0.026006 Scale est. = 0.023702 n = 60
#summary(FSG2)
#summary(FSG3)
summary(FSG4)
Family: gaussian
Link function: identity
Formula:
psd ~ s(depth, k = 3) + s(Hour, k = 4, bs = "cc")
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.96083 0.01995 -198.5 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(depth) 1.715 1.919 55.141 7.66e-15 ***
s(Hour) 1.565 2.000 2.497 0.0488 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) = 0.632 Deviance explained = 65.2%
GCV = 0.02572 Scale est. = 0.023885 n = 60
summary(FSG1)$r.sq - summary(FSG2)$r.sq
[1] 0.03804023
summary(FSG2)$r.sq - summary(FSG3)$r.sq
[1] -0.004905911
summary(FSG3)$r.sq
[1] 0.6015425
Not a significant difference in PSD with respect to time.
But there is between projects:
ProjGam <- gam(psd~ s(depth, k = 3) + factor(project), knots = list(Hour = c(0, 24)), data = bdsAddTime %>% filter(depth >= 175 & depth <=500))
summary(ProjGam)
Family: gaussian
Link function: identity
Formula:
psd ~ s(depth, k = 3) + factor(project)
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.96167 0.02491 -159.029 <2e-16 ***
factor(project)P16 -0.19977 0.07708 -2.592 0.0118 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(depth) 1.224 1.398 34.54 2.26e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) = 0.437 Deviance explained = 45.6%
GCV = 0.039117 Scale est. = 0.037234 n = 67
I wonder if I can show that the profiles aren’t statistically significanlty different. Or that they are for that matter… I think in that case, I run a gam with and without a parameter for profile… And then quantify the effect size of that parameter
Or follow this Gavin Simpson Post https://fromthebottomoftheheap.net/2017/10/10/difference-splines-i/
or anova.gam {mgcv}
Calculate gams for each profile, and then run anova.gam to see if they are different…
PlotNParticlesEP <- uds %>%
filter(profile %in% c("stn_043", "p16n_100")) %>%
ggplot(aes(x = tot_nparticles, y = depth, col = project, shape = project)) +
geom_point(alpha = 0.7, size = 2, stroke = 1) +
#geom_path(aes(x = tot_nparticles)) +
#geom_ribbon(aes(x = psd_gam, xmin = psd_gam - 2 * psd_seg, xmax = psd_gam + 2 * psd_seg), alpha = 0.1) +
scale_y_reverse(limits = c(2500, 0)) + scale_x_log10() + scale_color_manual(values = c("gray20", "brown")) +
labs(x = "Particles/L", y = "Depth (m)") +
theme(legend.position = "none") +
scale_shape_manual(values = c(1:5))
PlotNParticlesEP

I removed one outlyer from p16 for visualization purposes (300 particles/l at surface)
PlotPSDEP <- uds %>%
filter(profile %in% c("stn_043", "p16n_100")) %>%
ggplot(aes(x = psd, y = depth, col = project, shape = project)) +
geom_point(alpha = 0.7, size = 2, stroke = 1) +
geom_path(aes(x = psd_gam)) +
geom_ribbon(aes(x = psd_gam, xmin = psd_gam - 2 * psd_seg, xmax = psd_gam + 2 * psd_seg), alpha = 0.1) +
scale_y_reverse(limits = c(2500, 0)) + scale_color_manual(values = c("gray20", "brown")) +
scale_shape_manual(values = c(1:5)) + labs(y = "", x = "Particle Size Distribution Slope")
PlotPSDEP

I may just cow these togther.
plot_grid(PlotNParticlesEP, PlotPSDEP, rel_widths = c(2,3), labels = c("A", "B"))
Removed 611 rows containing missing values (geom_point).Removed 611 rows containing missing values (geom_point).Removed 611 row(s) containing missing values (geom_path).

ggsave("figures/ParticlesAndPSD_ETNPVsP16.svg")
Saving 10 x 4 in image
ggsave("figures/ParticlesAndPSD_ETNPVsP16.png")
Saving 10 x 4 in image
mainParticleComponents <- bds %>%
filter(profile %in% c("stn_043", "p16n_100")) %>%
select(project, profile, depth,
tot_nparticles, small_nparticles, big_nparticles,
tot_psd = psd, small_psd, big_psd,
tot_flux_fit, small_flux_fit, big_flux_fit) %>%
pivot_longer(cols = -c("project", "profile", "depth")) %>%
separate(name, c("size", "meas")) %>%
mutate(meas = recode(meas, nparticles = "particles/L")) %>%
mutate(meas = factor(meas, levels = c("particles/L", "flux", "psd")))
Error in (function (classes, fdef, mtable) :
unable to find an inherited method for function ‘select’ for signature ‘"spec_tbl_df"’
Flux small and flux tot track so closely because ag > psd. since the size distribution of the flux sould be PSD + ag (psd is negative in this case). Yo ucan see the variance at the one depth where psd is flatest at the very top.
eg_dataline <- bds %>%
filter(profile == "stn_043", depth == 162.5)
eg_slope = eg_dataline %>% pull(psd)
eg_icp = eg_dataline %>% pull(icp)
eg_vol = eg_dataline %>% pull(vol)
eg_datablock <- bes %>%
filter(profile == "stn_043", depth == 162.5)
eg_lb = eg_datablock$lb
eg_binsize = eg_datablock$binsize
eg_nnp = exp(eg_icp + log(eg_lb) * eg_slope)
eg_np = eg_nnp * eg_binsize
eg_tp = eg_np * eg_vol
eg_df <- tibble(lb = eg_lb, n_nparticles = eg_nnp, nparticles = eg_np, TotalParticles = eg_tp)
EgNNP <- eg_datablock %>%
ggplot(aes(x = lb, y = n_nparticles)) + geom_point() + scale_x_log10() + scale_y_log10() +
geom_path(data = eg_df) + labs(y = "Binsize & Volume Normalized \n Particles (#/L/mm)", x = "Size (mm)")
EgNP <- eg_datablock %>%
ggplot(aes(x = lb, y = nparticles)) + geom_point() + scale_x_log10() + scale_y_log10() +
geom_path(data = eg_df) + labs(y = "Normalized Particles" , x = "Size (mm)")
EgTP <- eg_datablock %>%
ggplot(aes(x = lb, y = TotalParticles)) + geom_point() + scale_x_log10() + scale_y_log10() +
geom_path(data = eg_df) + labs( y = "Total Particles Observed (#)", x = "Size (mm)")
plot_grid(EgNNP, EgTP, labels = c("A", "B"))
Transformation introduced infinite values in continuous y-axisTransformation introduced infinite values in continuous y-axis
ggsave("figures/ExamplePSD163m.png")
Saving 7.29 x 4.5 in image
ggsave("figures/ExamplePSD163m.svg")
Saving 7.29 x 4.5 in image

Smooth flux and um disaggregation.
bds %>%
ggplot(aes(y = depth, x = Flux_Smooth, col = factor(time))) + facet_wrap(~project) + geom_point() + scale_y_reverse(limits = c(1000, 0)) + scale_x_log10()

bdsAddTime <- bds %>%
mutate(Hour = hour(time), Day = day(time))
FSG1 <- gam(Flux_Smooth~ s(depth, k = 3) + s(Day, k = 3) + s(Hour, k = 4, bs = "cc"), knots = list(Hour = c(0, 24)), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))
FSG2 <- gam(Flux_Smooth ~ s(depth, k = 3) + s(Day, k = 3), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))
FSG3 <- gam(Flux_Smooth ~ s(depth, k = 3), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))
summary(FSG1)
Family: gaussian
Link function: identity
Formula:
Flux_Smooth ~ s(depth, k = 3) + s(Day, k = 3) + s(Hour, k = 4,
bs = "cc")
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 34.7319 0.9174 37.86 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(depth) 1.878 1.985 12.389 8.56e-05 ***
s(Day) 1.881 1.985 3.235 0.0416 *
s(Hour) 1.454 2.000 2.926 0.0233 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) = 0.352 Deviance explained = 41%
GCV = 56.33 Scale est. = 50.497 n = 60
summary(FSG2)
Family: gaussian
Link function: identity
Formula:
Flux_Smooth ~ s(depth, k = 3) + s(Day, k = 3)
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 34.7319 0.9663 35.94 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(depth) 1.867 1.982 11.055 0.000216 ***
s(Day) 1.814 1.965 2.125 0.128023
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) = 0.282 Deviance explained = 32.6%
GCV = 60.77 Scale est. = 56.029 n = 60
summary(FSG3)
Family: gaussian
Link function: identity
Formula:
Flux_Smooth ~ s(depth, k = 3)
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 34.7319 0.9944 34.93 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(depth) 1.857 1.979 10.73 0.000277 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) = 0.239 Deviance explained = 26.3%
GCV = 62.291 Scale est. = 59.325 n = 60
summary(FSG1)$r.sq - summary(FSG2)$r.sq
[1] 0.0709392
summary(FSG2)$r.sq - summary(FSG3)$r.sq
[1] 0.04226961
summary(FSG3)$r.sq
[1] 0.2392424
bds %>% filter(project == "ETNP") %>% select(profile, depth, Flux_Smooth) %>% pivot_wider(names_from = profile, values_from = Flux_Smooth)
Something is off. All of the flux profiles are identical. Skip this
cb10 <- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a')
plt1 <- bds %>% #filter(DFP > 1) %>% #filter(profile %in% c("stn_043", "p16n_100")) %>%
ggplot(aes(y = depth, x = DFP, col = factor(time), shape = factor(time))) + facet_wrap(~project) + geom_point() + scale_y_reverse(limits = c(1000, 0)) + xlim(c(0.5, 1.5))+ geom_vline(xintercept = 1) +
scale_color_manual(values = c(rep("black", 5), rep("blue", 5))) + scale_shape_manual(values = rep(1:5, 2))
plotly::ggplotly(plt1)
What the heck is going on with DFP here. Why is it usually > 1 shouldn’t it be less than 1 when flux is decreasing? This very deep increasing flux seems improbable to me. Lets check the smooths. Or only go to 1000m.
, legend.background = element_blank(), legend.box.background = element_rect()
scientific_10 <- function(x) {parse(text=gsub("e\\+*", " %*% 10^", scales::scientific_format()(x))) }
#https://stackoverflow.com/questions/10762287/how-can-i-format-axis-labels-with-exponents-with-ggplot2-and-scales
#jacob_magnitude <- function(x){expression(10^round(log10(x)))}
cb10 <- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a')
pltFlx <- bds %>% filter(project == "ETNP") %>% #filter(DFP > 1) %>% #filter(profile %in% c("stn_043", "p16n_100")) %>%
ggplot(aes(y = depth, x = Flux_Smooth, shape = factor(day(time)), fill = hour(time), group = factor(time))) + geom_point(size = 3, stroke = 1)+
geom_path() +
scale_y_reverse(limits = c(1000, 0))+
scale_x_log10(label = scientific_10) +
scale_color_gradient2(low = "darkgreen", mid = "gray80", high = "purple", midpoint = 10) + scale_shape_manual(name = "Day of Month", values = rep(21:25, 2)) +
scale_fill_gradientn(name = "Hour of Day", breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) +
labs(x = bquote(Smoothed~Flux~(µmol~C/m^2/d)), y = "Depth (m)") +
geom_rect(data = data.frame(project = "ETNP"), aes(xmin = 20, xmax = 180, ymin = 75, ymax = 500), colour = "red", fill = NA, inherit.aes = FALSE) +
theme(axis.text.x = element_text(angle = 90, vjust = .3), legend.spacing = unit(.1, "cm"))
pltFlxNoLegend <- pltFlx + theme(legend.position = "none")
pltFlxLegend <- get_legend(pltFlx)
Removed 14 rows containing missing values (geom_point).Removed 14 row(s) containing missing values (geom_path).
pltFlx

#plotly::ggplotly(plt1)
cb10 <- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a')
pltFlxZoom <- bds %>% filter(project == "ETNP" & depth <= 500 & depth >= 75) %>% #filter(profile %in% c("stn_043", "p16n_100")) %>%
ggplot(aes(y = depth, x = Flux_Smooth, shape = factor(day(time)), fill = hour(time), group = factor(time))) + geom_point(size = 3, stroke = 1)+
geom_path() +
scale_y_reverse()+
#scale_x_log10() +
scale_x_log10(breaks = c(seq(from = 20, to = 50, by = 10), seq(from = 60, to = 180, by = 20)), limits = c(20, 180)) +
scale_color_gradient2(low = "darkgreen", mid = "gray80", high = "purple", midpoint = 10) + scale_shape_manual(values = rep(21:25, 2)) +
scale_fill_gradientn(breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) +
theme(axis.text.x = element_text(angle = 90)) +
labs(x = "Smoothed Flux", y = "Depth") + theme(legend.position = "none")
pltFlxZoom

#plotly::ggplotly(plt1)
cb10 <- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a')
pltDelta3 <- bds %>% filter(project == "ETNP") %>% #filter(DFP > 1) %>% #filter(profile %in% c("stn_043", "p16n_100")) %>%
ggplot(aes(y = depth, x = pracma::nthroot(DF/DZ, 5), shape = factor(day(time)), fill = hour(time), group = factor(time))) + geom_point(size = 3, stroke = 1)+
geom_path() +
scale_y_reverse(limits = c(1000, 0))+
scale_x_continuous(limits = c(-2.1, .6), breaks = seq(from = -2, to = .75, by = 0.5)) +
#scale_x_log10() +
scale_color_gradient2(low = "darkgreen", mid = "gray80", high = "purple", midpoint = 10) + scale_shape_manual(name = "Day of Month", values = rep(21:25, 2)) +
scale_fill_gradientn(name = "Hour", breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) +
geom_vline(xintercept = 0) +
labs(x = bquote((DF/DZ)^{1/5}~(µmolC/m^3/d)^{1/5}), y = "Depth (m)") + theme(legend.pos = "none")
#labs(x = "(DF/DZ) ^ 1/5 (µmol C/m^3/d) ^ 1/5")
pltDelta3

#plotly::ggplotly(plt1pos)
# #plot_grid(pltFlxNoLegend, pltFlxZoom, pltDelta3, pltFlxLegend)
#
# pltFlxLegend <- get_legend(pltFlx + theme(legend.box.margin = margin(0, 0, 40, 10)))
#
# pgTop <- plot_grid(pltFlxNoLegend, pltFlxZoom + theme(plot.margin = unit(c(1, 0, 3, 0), units = "cm")), rel_widths = c(2, 1), labels = c("A", "B"))
# pgBottom <- plot_grid(pltDelta3, pltFlxLegend , rel_widths = c(3, 1), labels = c("C", ""), label_size = 14)
# pgBoth <- plot_grid(pgTop, pgBottom, ncol = 1)
#
# pgBoth
#
# ggsave("figures/FluxDeepDive.png")
# ggsave("figures/FluxDeepDive.svg")
Within panel drawing
pgTop <- ggdraw(pltFlxNoLegend
) +
draw_plot(pltFlxZoom, .4, .25, .55, .60) +
draw_plot_label(
c("","B"),
c(.05, 0.55),
c(1, 0.85),
size = 16
)
Removed 14 rows containing missing values (geom_point).Removed 14 row(s) containing missing values (geom_path).
pgTop

pgBottom <- plot_grid(pltDelta3, pltFlxLegend , rel_widths = c(3, 1), labels = c(“C”, ""), label_size = 14)
I don’t know whats going on below here
pgBottom <- pltDelta3 + geom_rect(aes(xmin = -2, xmax = -1.15, ymin = 170, ymax = 1000), colour = "gray50", fill = NA, inherit.aes = FALSE) + draw_plot(pltFlxLegend , -1.9, -575, .7)
pgBoth <- plot_grid(pgTop + theme(plot.margin = unit(c(0, 0, 0, 0), units = "cm")),
pgBottom + theme(plot.margin = unit(c(0, 0, 0, 0), units = "cm")),
ncol = 1, rel_heights = c(4, 4), labels = c("A", "C"), label_size = 16)
Removed 33 rows containing missing values (geom_point).Removed 33 row(s) containing missing values (geom_path).
pgBoth

ggsave("figures/FluxDeepDive.png")
Saving 5 x 9 in image
ggsave("figures/FluxDeepDive.svg")
Saving 5 x 9 in image
# #plot_grid(pltFlxNoLegend, pltFlxZoom, pltDelta3, pltFlxLegend)
#
# pltFlxLegend <- get_legend(pltFlx + theme(legend.box.margin = margin(0, 0, 40, 10)))
#
# pgTop <- plot_grid(pltFlxNoLegend + ylim(c(1000, 0)), pltFlxZoom + theme(plot.margin = unit(c(1, 0, 3, 0), units = "cm")), rel_widths = c(2, 1), labels = c("A", "B"))
# pgBottom <- plot_grid(pltDelta3 + ylim(c(1000, 0)), pltFlxLegend , rel_widths = c(3, 1), labels = c("C", ""))
# pgBoth <- plot_grid(pgTop, pgBottom, ncol = 1)
#
# pgBoth
#
# #ggsave("figures/FluxShallowDive.png")
# #ggsave("figures/FluxShallowDive.svg")
Test for day to day and hourly variability
bdsAddTime <- bds %>%
mutate(Hour = hour(time), Day = day(time))
DFG1 <- gam(DF/DZ~ s(depth, k = 3) + s(Day, k = 3) + s(Hour, k = 4, bs = "cc"), knots = list(Hour = c(0, 24)), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))
DFG2 <- gam(DF/DZ ~ s(depth, k = 3) + s(Day, k = 3), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))
DFG3 <- gam(DF/DZ ~ s(depth, k = 3), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))
summary(DFG1)
Family: gaussian
Link function: identity
Formula:
DF/DZ ~ s(depth, k = 3) + s(Day, k = 3) + s(Hour, k = 4, bs = "cc")
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.1001 0.0173 -5.785 3.8e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(depth) 1.907 1.991 22.926 8.57e-08 ***
s(Day) 1.908 1.991 6.576 0.00212 **
s(Hour) 1.329 2.000 2.351 0.03593 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) = 0.502 Deviance explained = 54.5%
GCV = 0.020011 Scale est. = 0.017961 n = 60
summary(DFG2)
Family: gaussian
Link function: identity
Formula:
DF/DZ ~ s(depth, k = 3) + s(Day, k = 3)
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.10008 0.01804 -5.547 8.5e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(depth) 1.897 1.989 21.470 2.11e-07 ***
s(Day) 1.903 1.991 4.847 0.00988 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) = 0.458 Deviance explained = 49.3%
GCV = 0.021234 Scale est. = 0.019535 n = 60
summary(DFG3)
Family: gaussian
Link function: identity
Formula:
DF/DZ ~ s(depth, k = 3)
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.10008 0.01944 -5.149 3.37e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(depth) 1.878 1.985 19.24 9.12e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) = 0.371 Deviance explained = 39.1%
GCV = 0.023812 Scale est. = 0.02267 n = 60
summary(DFG1)$r.sq - summary(DFG2)$r.sq
[1] 0.04364864
summary(DFG2)$r.sq - summary(DFG3)$r.sq
[1] 0.08693722
summary(DFG3)$r.sq
[1] 0.3713016
png(filename = “./figures/CombinedP2Info.png”, width = 10, height = 8, units = “in”, res = 200) StationInfoPlot() dev.off()
#plot.new()
FluxGamPlot <- function(){
par(mfrow = c(2,2))
plot(DFG1)
mtext(expression(bold("C")), side = 3, line = 0, adj = 0, cex = 2)
par(mfg = c(1,1))
mtext(expression(bold("A")), side = 3, line = 0, adj = 0, cex = 2)
par(mfg = c(1,2))
mtext(expression(bold("B")), side = 3, line = 0, adj = 0, cex = 2)
}
FluxGamPlot()
png(filename = "./figures/FluxGamPlot.png", width = 10, height = 8, units = "in", res = 200)
FluxGamPlot()
dev.off()
png
2

#Osps
(u mol C / m^3 / day)
bds %>% filter(project == "ETNP") %>%
ggplot(aes(y = depth, x = pracma::nthroot(ospsDZ, 3), shape = factor(day(time)), fill = hour(time), group = factor(time))) + geom_point(size = 2) + scale_y_reverse(limits = c(1000, 0)) +
scale_x_continuous(limits = c(-1, 1)) +
geom_vline(xintercept = 0) + scale_shape_manual(name = "Day of Month", values = rep(21:25, 2)) + labs(x = bquote("Observed - Modeled Small Particle Flux"~(μmol/m^3/day))) +
scale_fill_gradientn(name = "Hour of Day", breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) + geom_hline(yintercept = 175, color = "darkgreen") + geom_hline(yintercept = 950, color = "darkblue")

#ggsave("..figures/FluxSizeShift.svg"
ggsave("figures/FluxSizeShift.png")
Saving 6 x 4 in image
ggsave("figures/FluxSizeShift.svg")
Saving 6 x 4 in image
bdsAddTime <- bds %>%
mutate(Hour = hour(time), Day = day(time))
OZG1 <- gam(ospsDZ ~ s(depth, k = 3) + s(Day, k = 3) + s(Hour, k = 4, bs = "cc"), knots = list(Hour = c(0, 24)), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))
OZG2 <- gam(ospsDZ ~ s(depth, k = 3) + s(Day, k = 3), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))
OZG3 <- gam(ospsDZ ~ s(depth, k = 3), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))
summary(OZG1)
Family: gaussian
Link function: identity
Formula:
ospsDZ ~ s(depth, k = 3) + s(Day, k = 3) + s(Hour, k = 4, bs = "cc")
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.024817 0.002255 11.01 1.77e-15 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(depth) 1.8900 1.988 8.511 0.000405 ***
s(Day) 1.9131 1.992 6.058 0.003482 **
s(Hour) 0.5495 2.000 0.407 0.235090
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) = 0.341 Deviance explained = 39%
GCV = 0.00033503 Scale est. = 0.00030514 n = 60
summary(OZG2)
Family: gaussian
Link function: identity
Formula:
ospsDZ ~ s(depth, k = 3) + s(Day, k = 3)
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.024817 0.002272 10.92 2.06e-15 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(depth) 1.889 1.988 8.417 0.000432 ***
s(Day) 1.910 1.992 5.671 0.004881 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) = 0.331 Deviance explained = 37.4%
GCV = 0.00033672 Scale est. = 0.00030979 n = 60
summary(OZG3)
Family: gaussian
Link function: identity
Formula:
ospsDZ ~ s(depth, k = 3)
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.024817 0.002479 10.01 3.55e-14 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(depth) 1.872 1.984 7.338 0.000983 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) = 0.204 Deviance explained = 22.9%
GCV = 0.00038738 Scale est. = 0.00036884 n = 60
summary(OZG1)$r.sq - summary(OZG2)$r.sq
[1] 0.01003818
summary(OZG2)$r.sq - summary(OZG3)$r.sq
[1] 0.1274581
summary(OZG3)$r.sq
[1] 0.2038316
OSMSGamPlot <- function(){
par(mfrow = c(1,2))
plot(OZG2)
mtext(expression(bold("B")), side = 3, line = 0, adj = 0, cex = 2)
par(mfg = c(1,1))
mtext(expression(bold("A")), side = 3, line = 0, adj = 0, cex = 2)
}
OSMSGamPlot()
png(filename = "./figures/OSMSGamPlot.png", width = 10, height = 6, units = "in", res = 200)
OSMSGamPlot()
dev.off()
png
2

bds %>% filter(project == "P16") %>%
ggplot(aes(y = depth, x = ospsDZ)) + facet_wrap(~project) + geom_point() + scale_y_reverse(limits = c(500, 0)) + geom_vline(xintercept = 0)
Error in eval(lhs, parent, parent) : object 'bds' not found
---
title: "R Notebook"
output: html_notebook
---

```{r}
library(tidyverse)
library(cowplot)
library(lubridate)
library(mgcv)
source("UVP_2017_library.R")
theme_set(theme_cowplot())
cb10 <- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a')
```


# Particles Only

# Read In Data

```{r}
bes<- read_csv("dataOut/binned_EachSize.csv")
bds <- read_csv("dataOut/binned_DepthSummary.csv")
ues <- read_csv("dataOut/unbinned_EachSize.csv")
uds <- read_csv("dataOut/unbinned_DepthSummary.csv")
```

```{r}
PhoticBase <- 160
OMZBase <- 850
```


# Summary Statistics
```{r}
PlotNParticles <- uds %>% 
  ggplot(aes(x = tot_nparticles, y = depth, col = profile)) +
 facet_wrap(~project) +
 geom_point(alpha = 0.3, shape = 1) +
scale_y_reverse() + scale_x_log10()

PlotNParticles
```

```{r}
bdsAddTime <- bds %>%
  mutate(Hour = hour(time), Day = day(time))

FSG1 <- gam(tot_nparticles~ s(depth, k = 3) + s(Day, k = 3) + s(Hour, k = 4, bs = "cc"), knots = list(Hour = c(0, 24)), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))

FSG2 <- gam(tot_nparticles ~ s(depth, k = 3) + s(Day, k = 3), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))

FSG3 <- gam(tot_nparticles ~ s(depth, k = 3), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))

#FSG4 <- gam(tot_nparticles~ s(depth, k = 3)  + s(Hour, k = 4, bs = "cc"), knots = list(Hour = c(0, 24)), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))

summary(FSG1)
#summary(FSG2)
#summary(FSG3)
#summary(FSG4)

summary(FSG1)$r.sq - summary(FSG2)$r.sq
summary(FSG2)$r.sq - summary(FSG3)$r.sq
summary(FSG3)$r.sq
```

But there is between projects:
```{r}
ProjGam <- gam(tot_nparticles~ s(depth, k = 3) + factor(project), knots = list(Hour = c(0, 24)), data = bdsAddTime %>% filter(depth >= 175 & depth <=500))

summary(ProjGam)
```

```{r fig.width = 12}
PlotPSDmany <- uds %>% 
  filter(project == "ETNP") %>%
  ggplot(aes(x = psd, y = depth, shape = factor(day(time)), fill = hour(time))) +
 
  #geom_path(aes(x = psd_gam)) + 
  #geom_ribbon(aes(x = psd_gam, xmin = psd_gam - 2 * psd_seg, xmax = psd_gam + 2 * psd_seg), alpha = 0.1, outline_type = "lower") +
  geom_point(alpha = .6, size = 2, stroke = 1) +
  scale_y_reverse() + scale_shape_manual(values = c(21:25)) +
  scale_fill_gradientn(breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) +
  labs(x = "Depth (m)", y = "Particle Size Distribution Slope")

PlotParticlesmany <- uds %>% 
  filter(project == "ETNP") %>%
  ggplot(aes(x = tot_nparticles, y = depth, shape = factor(day(time)), fill = hour(time))) +
 
  #geom_path(aes(x = psd_gam)) + 
  #geom_ribbon(aes(x = psd_gam, xmin = psd_gam - 2 * psd_seg, xmax = psd_gam + 2 * psd_seg), alpha = 0.1, outline_type = "lower") +
  geom_point(alpha = .6, size = 2, stroke = 1) +
  scale_y_reverse() + scale_shape_manual(values = c(21:25)) +
  scale_fill_gradientn(breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) +
  scale_x_log10() + theme(legend.position = "none") +
  labs(x = "Depth (m)", y = "Particles / L")

PlotFluxmany <- uds %>% 
  filter(project == "ETNP") %>%
  ggplot(aes(x = tot_flux_fit, y = depth, shape = factor(day(time)), fill = hour(time))) +
 
  #geom_path(aes(x = psd_gam)) + 
  #geom_ribbon(aes(x = psd_gam, xmin = psd_gam - 2 * psd_seg, xmax = psd_gam + 2 * psd_seg), alpha = 0.1, outline_type = "lower") +
  geom_point(alpha = .6, size = 2, stroke = 1) +
  scale_y_reverse() + scale_shape_manual(values = c(21:25)) +
  scale_fill_gradientn(breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) +
  scale_x_log10() + theme(legend.position = "none")



plot_grid(
  PlotParticlesmany,
  PlotPSDmany,
  rel_widths = c(2, 3)
  )

ggsave("figures/ParticlesPSDMany.png")
ggsave("figures/ParticlesPSDMany.svg")

```

```{r}
bdsAddTime <- bds %>%
  mutate(Hour = hour(time), Day = day(time))

FSG1 <- gam(psd~ s(depth, k = 3) + s(Day, k = 3) + s(Hour, k = 4, bs = "cc"), knots = list(Hour = c(0, 24)), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))

FSG2 <- gam(psd ~ s(depth, k = 3) + s(Day, k = 3), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))

FSG3 <- gam(psd ~ s(depth, k = 3), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))

FSG4 <- gam(psd~ s(depth, k = 3)  + s(Hour, k = 4, bs = "cc"), knots = list(Hour = c(0, 24)), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))

summary(FSG1)
#summary(FSG2)
#summary(FSG3)
summary(FSG4)

summary(FSG1)$r.sq - summary(FSG2)$r.sq
summary(FSG2)$r.sq - summary(FSG3)$r.sq
summary(FSG3)$r.sq
```
Not a significant difference in PSD with respect to time.

But there is between projects:
```{r}
ProjGam <- gam(psd~ s(depth, k = 3) + factor(project), knots = list(Hour = c(0, 24)), data = bdsAddTime %>% filter(depth >= 175 & depth <=500))

summary(ProjGam)
```




I wonder if I can show that the profiles aren't statistically significanlty different. Or that they are for that matter...
I think in that case, I run a gam with and without a parameter for profile...
And then quantify the effect size of that parameter

Or follow this Gavin Simpson Post
https://fromthebottomoftheheap.net/2017/10/10/difference-splines-i/

or 
anova.gam {mgcv}

Calculate gams for each profile, and then run anova.gam to see if they are different...

```{r}
PlotNParticlesEP <- uds %>% 
  filter(profile %in% c("stn_043", "p16n_100")) %>%
  ggplot(aes(x = tot_nparticles, y = depth, col = project, shape = project)) +
 geom_point(alpha = 0.7, size = 2, stroke = 1) +
  #geom_path(aes(x = tot_nparticles)) +
  #geom_ribbon(aes(x = psd_gam, xmin = psd_gam - 2 * psd_seg, xmax = psd_gam + 2 * psd_seg), alpha = 0.1) +
scale_y_reverse(limits = c(2500, 0)) + scale_x_log10() + scale_color_manual(values = c("gray20", "brown")) +
  labs(x = "Particles/L", y = "Depth (m)") +
  theme(legend.position = "none") +
  scale_shape_manual(values = c(1:5)) 

PlotNParticlesEP
```

I removed one outlyer from p16 for visualization purposes (300 particles/l at surface)

```{r}
PlotPSDEP <- uds %>% 
  filter(profile %in% c("stn_043", "p16n_100")) %>%
  ggplot(aes(x = psd, y = depth, col = project, shape = project)) +
 geom_point(alpha = 0.7, size = 2, stroke = 1) +
  geom_path(aes(x = psd_gam)) +
  geom_ribbon(aes(x = psd_gam, xmin = psd_gam - 2 * psd_seg, xmax = psd_gam + 2 * psd_seg), alpha = 0.1) +
scale_y_reverse(limits = c(2500, 0)) + scale_color_manual(values = c("gray20", "brown"))  +
  scale_shape_manual(values = c(1:5)) + labs(y = "", x = "Particle Size Distribution Slope")

PlotPSDEP
```

I may just cow these togther.

```{r fig.width = 10, fig.height = 4}
plot_grid(PlotNParticlesEP, PlotPSDEP, rel_widths = c(2,3), labels = c("A", "B"))
ggsave("figures/ParticlesAndPSD_ETNPVsP16.svg")
ggsave("figures/ParticlesAndPSD_ETNPVsP16.png")
```

```{r}
mainParticleComponents <- bds %>%
  filter(profile %in% c("stn_043", "p16n_100")) %>%
  select(project, profile, depth,
         tot_nparticles, small_nparticles, big_nparticles,
         tot_psd = psd, small_psd, big_psd,
         tot_flux_fit, small_flux_fit, big_flux_fit) %>%
  pivot_longer(cols = -c("project", "profile", "depth")) %>%
  separate(name, c("size", "meas")) %>%
  mutate(meas = recode(meas, nparticles = "particles/L")) %>%
  mutate(meas = factor(meas, levels = c("particles/L", "flux", "psd")))

PlotFlx <- mainParticleComponents %>% 
  filter(meas != "psd") %>%
  ggplot(aes(y = depth, x = value, col = project, shape = project)) + facet_grid(size ~ meas, scales = "free_x") + geom_point(size = 2) + scale_y_reverse(limits = c(1000, 0)) + scale_x_log10() + theme(axis.title.x = element_blank(), legend.position = "none", strip.background.y = element_blank(), strip.text.y = element_blank(), plot.margin = unit(c(7,0,7,7), "pt")) + scale_color_manual(values = c("brown", "gray20")) + scale_shape_manual(values = c(1:5)) + theme(axis.text.x = element_text(angle = 90)) + geom_hline(yintercept = 175, color = "darkgreen")

PlotPSD <- mainParticleComponents %>% 
  filter(meas == "psd") %>%
  ggplot(aes(y = depth, x = value, col = project, shape = project)) + facet_grid(size~meas, scales = "free_x") + geom_point(size = 2) + scale_y_reverse(limits = c(1000, 0)) +
  theme(axis.title.x = element_blank(), axis.title.y = element_blank(), axis.line.y = element_blank(), axis.text.y = element_blank(), axis.ticks.y = element_blank(), plot.margin = unit(c(7,7,26.5,0), "pt")) +
  scale_color_manual(values = c("brown", "gray20")) +  scale_shape_manual(values = c(1:5)) +  theme(axis.text.x = element_text(angle = 90)) + geom_hline(yintercept = 175, color = "darkgreen")

plot_grid(PlotFlx, PlotPSD, rel_widths = c(3, 2))

ggsave("figures/BigVsSmall.svg")
ggsave("figures/BigVsSmall.png")
```

Flux small and flux tot track so closely because ag > psd. since the size distribution of the flux sould be PSD + ag (psd is negative in this case). Yo ucan see the variance at the one depth where psd is flatest at the very top.

```{r}
eg_dataline <- bds %>% 
  filter(profile == "stn_043", depth == 162.5)
eg_slope =  eg_dataline %>% pull(psd)
eg_icp = eg_dataline %>% pull(icp)
eg_vol = eg_dataline %>% pull(vol)

eg_datablock <- bes %>%
  filter(profile == "stn_043", depth == 162.5)


eg_lb = eg_datablock$lb
eg_binsize = eg_datablock$binsize
eg_nnp = exp(eg_icp + log(eg_lb) * eg_slope)

eg_np = eg_nnp * eg_binsize
eg_tp = eg_np * eg_vol
eg_df <- tibble(lb = eg_lb, n_nparticles = eg_nnp, nparticles = eg_np, TotalParticles = eg_tp)


EgNNP <- eg_datablock %>%
  ggplot(aes(x = lb, y = n_nparticles)) + geom_point() + scale_x_log10() + scale_y_log10() + 
  geom_path(data = eg_df) + labs(y = "Binsize & Volume Normalized \n Particles (#/L/mm)", x = "Size (mm)")

EgNP <- eg_datablock %>%
  ggplot(aes(x = lb, y = nparticles)) + geom_point() + scale_x_log10() + scale_y_log10() + 
  geom_path(data = eg_df) + labs(y = "Normalized Particles" , x = "Size (mm)")

EgTP <- eg_datablock %>%
  ggplot(aes(x = lb, y = TotalParticles)) + geom_point() + scale_x_log10() + scale_y_log10() + 
  geom_path(data = eg_df) + labs( y = "Total Particles Observed (#)", x = "Size (mm)")

plot_grid(EgNNP, EgTP, labels = c("A", "B"))
ggsave("figures/ExamplePSD163m.png")
ggsave("figures/ExamplePSD163m.svg")

```

# Smooth flux and um disaggregation.

```{r}
bds %>% 
  ggplot(aes(y = depth, x = Flux_Smooth, col = factor(time))) + facet_wrap(~project) + geom_point() + scale_y_reverse(limits = c(1000, 0)) + scale_x_log10()
```




```{r}
bdsAddTime <- bds %>%
  mutate(Hour = hour(time), Day = day(time))

FSG1 <- gam(Flux_Smooth~ s(depth, k = 3) + s(Day, k = 3) + s(Hour, k = 4, bs = "cc"), knots = list(Hour = c(0, 24)), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))

FSG2 <- gam(Flux_Smooth ~ s(depth, k = 3) + s(Day, k = 3), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))

FSG3 <- gam(Flux_Smooth ~ s(depth, k = 3), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))

summary(FSG1)
summary(FSG2)
summary(FSG3)

summary(FSG1)$r.sq - summary(FSG2)$r.sq
summary(FSG2)$r.sq - summary(FSG3)$r.sq
summary(FSG3)$r.sq
```

```{r}
bds %>% filter(project == "ETNP") %>% select(profile, depth, Flux_Smooth) %>% pivot_wider(names_from = profile, values_from = Flux_Smooth)
```

Something is off. All of the flux profiles are identical.
Skip this
```{r}
cb10 <- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a')
plt1 <- bds %>% #filter(DFP > 1) %>% #filter(profile %in% c("stn_043", "p16n_100")) %>%
  ggplot(aes(y = depth, x = DFP, col = factor(time), shape = factor(time))) + facet_wrap(~project) + geom_point() + scale_y_reverse(limits = c(1000, 0)) + xlim(c(0.5, 1.5))+ geom_vline(xintercept = 1) +
   scale_color_manual(values = c(rep("black", 5), rep("blue", 5))) + scale_shape_manual(values = rep(1:5, 2))

plotly::ggplotly(plt1)
```

What the heck is going on with DFP here. Why is it usually > 1 shouldn't it be less than 1 when flux is decreasing?
This very deep increasing flux seems improbable to me.
Lets check the smooths. Or only go to 1000m.

, legend.background = element_blank(), legend.box.background = element_rect()
```{r fig.width=6, fig.height=4}
scientific_10 <- function(x) {parse(text=gsub("e\\+*", " %*% 10^", scales::scientific_format()(x))) }
#https://stackoverflow.com/questions/10762287/how-can-i-format-axis-labels-with-exponents-with-ggplot2-and-scales
#jacob_magnitude <- function(x){expression(10^round(log10(x)))}

cb10 <- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a')
pltFlx <- bds %>% filter(project == "ETNP") %>% #filter(DFP > 1) %>% #filter(profile %in% c("stn_043", "p16n_100")) %>%
  ggplot(aes(y = depth, x = Flux_Smooth, shape = factor(day(time)), fill = hour(time), group = factor(time)))  + geom_point(size = 3, stroke = 1)+
  geom_path() +
  scale_y_reverse(limits = c(1000, 0))+
  scale_x_log10(label = scientific_10) +
   scale_color_gradient2(low = "darkgreen", mid = "gray80", high = "purple", midpoint = 10) + scale_shape_manual(name = "Day of Month", values = rep(21:25, 2)) + 
  scale_fill_gradientn(name = "Hour of Day", breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) +
  
labs(x = bquote(Smoothed~Flux~(µmol~C/m^2/d)), y = "Depth (m)") +
  geom_rect(data = data.frame(project = "ETNP"), aes(xmin = 20, xmax = 180, ymin = 75, ymax = 500), colour = "red", fill = NA, inherit.aes = FALSE) +
  theme(axis.text.x = element_text(angle = 90, vjust = .3), legend.spacing = unit(.1, "cm"))



pltFlxNoLegend <- pltFlx + theme(legend.position = "none")
pltFlxLegend <- get_legend(pltFlx)

pltFlx
#plotly::ggplotly(plt1)
```

```{r fig.width=6, fig.height=4}
cb10 <- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a')
pltFlxZoom <- bds %>% filter(project == "ETNP" & depth <= 500 & depth >= 75) %>% #filter(profile %in% c("stn_043", "p16n_100")) %>%
  ggplot(aes(y = depth, x = Flux_Smooth, shape = factor(day(time)), fill = hour(time), group = factor(time))) + geom_point(size = 3, stroke = 1)+
  geom_path() +
  scale_y_reverse()+
  #scale_x_log10() +
  scale_x_log10(breaks = c(seq(from = 20, to = 50, by = 10), seq(from = 60, to = 180, by = 20)), limits = c(20, 180)) +
   scale_color_gradient2(low = "darkgreen", mid = "gray80", high = "purple", midpoint = 10) + scale_shape_manual(values = rep(21:25, 2)) + 
  scale_fill_gradientn(breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) +
  theme(axis.text.x = element_text(angle = 90)) +
labs(x = "Smoothed Flux", y = "Depth") + theme(legend.position = "none")

pltFlxZoom
#plotly::ggplotly(plt1)
```



```{r fig.width=6, fig.height=4}
cb10 <- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a')
pltDelta3 <- bds %>% filter(project == "ETNP") %>% #filter(DFP > 1) %>% #filter(profile %in% c("stn_043", "p16n_100")) %>%
  ggplot(aes(y = depth, x = pracma::nthroot(DF/DZ, 5), shape = factor(day(time)), fill = hour(time), group = factor(time)))  + geom_point(size = 3, stroke = 1)+
  geom_path() +
  scale_y_reverse(limits = c(1000, 0))+
  scale_x_continuous(limits = c(-2.1, .6), breaks = seq(from = -2, to = .75, by = 0.5)) +
  #scale_x_log10() +
   scale_color_gradient2(low = "darkgreen", mid = "gray80", high = "purple", midpoint = 10) + scale_shape_manual(name = "Day of Month", values = rep(21:25, 2)) + 
  scale_fill_gradientn(name = "Hour", breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) +
  geom_vline(xintercept = 0) +
  labs(x = bquote((DF/DZ)^{1/5}~(µmolC/m^3/d)^{1/5}), y = "Depth (m)") + theme(legend.pos = "none")
  #labs(x = "(DF/DZ) ^ 1/5 (µmol C/m^3/d) ^ 1/5")

pltDelta3
#plotly::ggplotly(plt1pos)
```



```{r fig.height=8, fig.width = 8}
# #plot_grid(pltFlxNoLegend, pltFlxZoom, pltDelta3, pltFlxLegend)
# 
# pltFlxLegend <- get_legend(pltFlx + theme(legend.box.margin = margin(0, 0, 40, 10)))
# 
# pgTop <- plot_grid(pltFlxNoLegend, pltFlxZoom + theme(plot.margin = unit(c(1, 0, 3, 0), units = "cm")), rel_widths = c(2, 1), labels = c("A", "B"))
# pgBottom <- plot_grid(pltDelta3, pltFlxLegend , rel_widths = c(3, 1), labels = c("C", ""), label_size = 14)
# pgBoth <- plot_grid(pgTop, pgBottom, ncol = 1)
# 
# pgBoth
# 
# ggsave("figures/FluxDeepDive.png")
# ggsave("figures/FluxDeepDive.svg")

```


Within panel drawing


```{r fig.height = 5, fig.width = 5}
pgTop <- ggdraw(pltFlxNoLegend 
       ) +
  draw_plot(pltFlxZoom, .4, .25, .55, .60) +
  draw_plot_label(
    c("","B"),
    c(.05, 0.55),
    c(1, 0.85),
    size = 16
  )
pgTop
```
pgBottom <- plot_grid(pltDelta3, pltFlxLegend , rel_widths = c(3, 1), labels = c("C", ""), label_size = 14)




I don't know whats going on below here

```{r fig.height = 9, fig.width = 5}
pgBottom <- pltDelta3  + geom_rect(aes(xmin = -2, xmax = -1.15, ymin = 170, ymax = 1000), colour = "gray50", fill = NA, inherit.aes = FALSE) + draw_plot(pltFlxLegend , -1.9, -575, .7)
pgBoth <- plot_grid(pgTop + theme(plot.margin = unit(c(0, 0, 0, 0), units = "cm")),
                    pgBottom + theme(plot.margin = unit(c(0, 0, 0, 0), units = "cm")),
                    ncol = 1, rel_heights = c(4, 4), labels = c("A", "C"), label_size = 16)
pgBoth

ggsave("figures/FluxDeepDive.png")
ggsave("figures/FluxDeepDive.svg")
```



```{r fig.height=8, fig.width = 8}
# #plot_grid(pltFlxNoLegend, pltFlxZoom, pltDelta3, pltFlxLegend)
# 
# pltFlxLegend <- get_legend(pltFlx + theme(legend.box.margin = margin(0, 0, 40, 10)))
# 
# pgTop <- plot_grid(pltFlxNoLegend + ylim(c(1000, 0)), pltFlxZoom + theme(plot.margin = unit(c(1, 0, 3, 0), units = "cm")), rel_widths = c(2, 1), labels = c("A", "B"))
# pgBottom <- plot_grid(pltDelta3 + ylim(c(1000, 0)), pltFlxLegend , rel_widths = c(3, 1), labels = c("C", ""))
# pgBoth <- plot_grid(pgTop, pgBottom, ncol = 1)
# 
# pgBoth
# 
# #ggsave("figures/FluxShallowDive.png")
# #ggsave("figures/FluxShallowDive.svg")

```

Test for day to day and hourly variability
```{r}
bdsAddTime <- bds %>%
  mutate(Hour = hour(time), Day = day(time))

DFG1 <- gam(DF/DZ~ s(depth, k = 3) + s(Day, k = 3) + s(Hour, k = 4, bs = "cc"), knots = list(Hour = c(0, 24)), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))

DFG2 <- gam(DF/DZ ~ s(depth, k = 3) + s(Day, k = 3), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))

DFG3 <- gam(DF/DZ ~ s(depth, k = 3), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))

summary(DFG1)
summary(DFG2)
summary(DFG3)

summary(DFG1)$r.sq - summary(DFG2)$r.sq
summary(DFG2)$r.sq - summary(DFG3)$r.sq
summary(DFG3)$r.sq
```
png(filename = "./figures/CombinedP2Info.png", width = 10, height = 8, units = "in", res = 200)
StationInfoPlot()
dev.off()
```{r fig.height = 8, fig.width = 10}
#plot.new()
FluxGamPlot <- function(){
  par(mfrow = c(2,2))
  plot(DFG1)
  mtext(expression(bold("C")), side = 3, line = 0, adj = 0, cex = 2)
  par(mfg = c(1,1))
  mtext(expression(bold("A")), side = 3, line = 0, adj = 0, cex = 2)
  par(mfg = c(1,2))
  mtext(expression(bold("B")), side = 3, line = 0, adj = 0, cex = 2)
}

FluxGamPlot()

png(filename = "./figures/FluxGamPlot.png", width = 10, height = 8, units = "in", res = 200)
FluxGamPlot()
dev.off()
```

#Osps

(u mol C / m^3 / day)
```{r fig.width = 6, fig.height = 4}
bds %>% filter(project == "ETNP") %>%
  ggplot(aes(y = depth, x = pracma::nthroot(ospsDZ, 3), shape = factor(day(time)), fill = hour(time), group = factor(time))) + geom_point(size = 2) + scale_y_reverse(limits = c(1000, 0)) +
  scale_x_continuous(limits = c(-1, 1)) +
  geom_vline(xintercept = 0) +   scale_shape_manual(name = "Day of Month", values = rep(21:25, 2)) + labs(x = bquote("Observed - Modeled Small Particle Flux"~(μmol/m^3/day))) +
  scale_fill_gradientn(name = "Hour of Day", breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) + geom_hline(yintercept = 175, color = "darkgreen") + geom_hline(yintercept = 950, color = "darkblue")

#ggsave("..figures/FluxSizeShift.svg"

 ggsave("figures/FluxSizeShift.png")
 ggsave("figures/FluxSizeShift.svg")
```

```{r}
bdsAddTime <- bds %>%
  mutate(Hour = hour(time), Day = day(time))

OZG1 <- gam(ospsDZ ~ s(depth, k = 3) + s(Day, k = 3) + s(Hour, k = 4, bs = "cc"), knots = list(Hour = c(0, 24)), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))

OZG2 <- gam(ospsDZ ~ s(depth, k = 3) + s(Day, k = 3), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))

OZG3 <- gam(ospsDZ ~ s(depth, k = 3), data = bdsAddTime %>% filter(depth >= 175 & depth <=500 & project == "ETNP"))

summary(OZG1)
summary(OZG2)
summary(OZG3)

summary(OZG1)$r.sq - summary(OZG2)$r.sq
summary(OZG2)$r.sq - summary(OZG3)$r.sq
summary(OZG3)$r.sq
```

```{r}
OSMSGamPlot <- function(){
  par(mfrow = c(1,2))
  plot(OZG2)
  mtext(expression(bold("B")), side = 3, line = 0, adj = 0, cex = 2)
  par(mfg = c(1,1))
  mtext(expression(bold("A")), side = 3, line = 0, adj = 0, cex = 2)
}

OSMSGamPlot()

png(filename = "./figures/OSMSGamPlot.png", width = 10, height = 6, units = "in", res = 200)
OSMSGamPlot()

dev.off()
```

```{r}
plot(OZG2)
```


```{r}
bds %>% filter(project == "P16") %>%
  ggplot(aes(y = depth, x = ospsDZ)) + facet_wrap(~project) + geom_point() + scale_y_reverse(limits = c(500, 0)) + geom_vline(xintercept = 0)
```

# Trap data
```{r}
trapFlux3 <- read_csv("dataOut/fluxMS_distilled.csv")
UVPFluxComb <- read_csv("dataOut/CombinedProfileFluxEst_DS.csv")
UVPFluxOE <- read_csv("dataOut/ObservedVsExpectedFlux.csv")

```

```{r}
trapFlux3
```
```{r}
UVPFluxComb
```


```{r}

fluxMS_distilled_toPlot <- trapFlux3 %>%
  mutate(SampleType = recode(SampleType, `plus.p` = "plus-particles", top = "top-collector"))
```

```{r}
UVPFluxComb %>%
  ggplot(aes(y = depth))  + scale_y_reverse(limits = c(1000, 0)) +
  scale_x_continuous(limits = c(0, 200)) +
  geom_point(aes(y = Depth, x = C_flux_umol, fill = SampleType, shape = TrapType),
             colour = "black", stroke = 1, size = 5, data = fluxMS_distilled_toPlot) +
  geom_point(aes(x = Flux), size = 3, shape = 21, color = "white", fill = "black") +
  geom_point(aes(x = -1, y = -1, size = "UVP")) + # dummy point for the legend
scale_shape_manual(values = c(25, 22))+
  scale_size_manual(values = 1, name = "") +
  ylab("Depth (m)") + xlab("Flux µmolC/m^2/day") +
  guides(fill = guide_legend(override.aes = list(shape = 21))) +
  theme_cowplot() + 
  theme(
        legend.position = c(0.5, 0.4),
        legend.box.background = element_rect(color = "black", size = 0.5),
        legend.margin = margin(-10, 5, 10, 5)
  ) +
  geom_rect(data = data.frame(project = "ETNP"), aes(xmin = 15, xmax = 32, ymin = 45, ymax = 195), colour = "red", fill = NA, inherit.aes = FALSE)
# ggsave("figures/FittedFlux.png")
# ggsave("figures/FittedFlux.svg")
```

```{r}
UVPFluxComb %>%
  ggplot(aes(y = depth))  + scale_y_reverse(limits = c(1000, 0)) +
  scale_x_continuous(limits = c(0, 200)) +
  geom_point(aes(y = Depth, x = C_flux_umol, fill = SampleType, shape = TrapType),
             colour = "black", stroke = 1, size = 5, data = fluxMS_distilled_toPlot) +
  geom_line(aes(x = Flux), size = 1, color = "black") +
  geom_point(aes(x = -1, y = -1, size = "UVP Estimate")) + # dummy point for the legend
  geom_point(aes(x = tot_flux2), size = 3, shape = 21, color = "white", fill = "black", data = UVPFluxOE) +
scale_shape_manual(values = c(25, 22))+
  scale_size_manual(values = 1, name = "") +
  ylab("Depth (m)") + xlab("Flux µmolC/m^2/day") +
  guides(fill = guide_legend(override.aes = list(shape = 21))) +
  theme_cowplot() + 
  theme(
        legend.position = c(0.5, 0.4),
        legend.box.background = element_rect(color = "black", size = 0.5),
        legend.margin = margin(-10, 5, 10, 5)
  ) +
  geom_rect(data = data.frame(project = "ETNP"), aes(xmin = 15, xmax = 32, ymin = 45, ymax = 195), colour = "red", fill = NA, inherit.aes = FALSE)
ggsave("figures/FittedFlux.png")
ggsave("figures/FittedFlux.svg")
```

## Example particle size distribution

horizontalGamPlot <- dataGamHorizontal %>% ggplot(aes(x = resp_fit, y = depth, col = log(lb), group = lb)) + scale_y_reverse() + geom_point() + scale_x_log10(limits = c(10^-8, NA)) + scale_color_viridis_c() + geom_path() + geom_vline(xintercept = 1) + geom_vline(xintercept = 5)  + geom_errorbar(aes(xmin = resp_lower, xmax = resp_upper), width = 10, alpha = 0.5)+ theme_bw()

```{r fig.width= 10}
TPPlot <- bes %>% filter(profile == "stn_043") %>% group_by(lb) %>% ggplot(aes(x = TotalParticles, y = depth, col = log(lb), group = lb)) + scale_y_reverse(limits = c(1000, 0)) + geom_point() + scale_x_log10() + scale_color_viridis_c() + geom_path() + geom_vline(xintercept = 1) + geom_vline(xintercept = 5) + labs(y = "Depth (m)", x = "TotalParticles Observed (#)")

nnpPlot <- bes %>% filter(profile == "stn_043") %>% group_by(lb) %>% ggplot(aes(x = n_nparticles, y = depth, col = log(lb), group = lb)) + scale_y_reverse(limits = c(1000, 0)) + geom_point() + scale_x_log10() + scale_color_viridis_c() + geom_path() + geom_vline(xintercept = 1) + geom_vline(xintercept = 5) + labs(y = "Depth (m)", x = "Binsize and Volume Normalized Particles (#/L/mm)")

FitPlot <- bes %>% filter(profile == "stn_043") %>% group_by(lb) %>% ggplot(aes(x = nnp_smooth, xmin = nnp_lower, xmax = nnp_upper, y = depth, col = log(lb), group = lb)) + scale_y_reverse(limits = c(1000, 0)) + geom_point() + scale_x_log10() + scale_color_viridis_c() + geom_path() + geom_vline(xintercept = 1) + geom_vline(xintercept = 5) + labs(y = "Depth (m)", x = "Smoothed - Normalized Particles (#/L/mm)") + geom_errorbar(width = 10, alpha = 0.5)

npLegend <- get_legend(FitPlot + theme(legend.box.margin = margin(0, 0, 40, 200)) + labs(col = expression(log[e](Size (mm)))))

plot_grid(
  TPPlot + theme(legend.position = "none"),
  nnpPlot + theme(legend.position = "none"),
  npLegend ,
  FitPlot + theme(legend.position = "none")
)

ggsave("figures/AllParticleSizes.svg")
```


## Weber Bianchi Figs




```{r}
SameGam <- gam(TotalParticles ~s(log(lb), log(depth), by = factor(time)), offset = log(vol * binsize), family = nb(),
    data = bes %>% filter(project == "ETNP"))
```

```{r}
besE <- bes %>% filter(project == "ETNP")

lb_new <- exp(seq(from = log(0.1), to = log(2.1), by = 0.05))
ub_new <- lead(lb_new)
binsize_new <- ub_new - lb_new

lbbs <- tibble(lb = lb_new, ub = ub_new, binsize = binsize_new)

Expanded <- expand_grid(lb = exp(seq(from = log(0.1), to = log(2), by = 0.05)), depth = seq(from = 20, to = 2000, by = 20), time = as.factor(unique(besE$time))) %>% left_join(lbbs, by = "lb")

Pred <- exp(predict(SameGam, Expanded))
ToPlot <- bind_cols(Expanded, nnparticles = Pred) %>% mutate(time = as.character(time)) %>% mutate(nparticles = nnparticles * binsize)
```

```{r}
ToPlot %>% filter(lb <= 2) %>%  ggplot(aes(x = lb, y = depth, fill = log10(nnparticles), z = log10(nnparticles))) + geom_tile() + scale_fill_viridis_c() + scale_y_reverse() + scale_x_log10()  + facet_wrap(~time) + geom_contour(color = "black")
```
```{r}
meanBese <- ToPlot %>% filter(lb <= 2) %>% group_by(lb, depth) %>% summarize(nparticles = mean(nparticles), nnparticles = mean(nnparticles))

WBColorMap <- meanBese%>%
   ggplot(aes(x = lb, y = depth, fill = log10(nnparticles), z = log10(nnparticles))) + geom_tile() + scale_fill_viridis_c(name = "log10(number density \n (normalized))") + scale_y_reverse() + scale_x_log10() + geom_contour(color = "black") + geom_hline(yintercept = 160, color = "darkgreen") + geom_hline(yintercept = 850, color = "darkblue")
WBColorMap
```
Average of everything

```{r}
meanBese043 <- ToPlot %>% filter(lb <= 2, time == "2017-01-13 11:51:31")

meanBese043%>%
   ggplot(aes(x = lb, y = depth, fill = log10(nnparticles), z = log10(nnparticles))) + geom_tile() + scale_fill_viridis_c() + scale_y_reverse() + scale_x_log10() + geom_contour(color = "black") + geom_hline(yintercept = 160, color = "darkgreen")
```
Just 043

```{r}
mbGam <- meanBese %>% group_by(depth)  %>% nest() %>%
  mutate(mod = map(data, ~gam(log(nnparticles) ~ log(lb), family = gaussian(), data = .))) %>% 
  mutate(psd = map_dbl(mod, ~summary(.)$p.coeff[2]))
```

```{r}
mbGam %>% ggplot(aes(x = psd, y = depth)) + geom_path() + scale_y_reverse() + geom_hline(yintercept = 160, color = "darkgreen") + geom_hline(yintercept =  850, color = "darkblue")
```

```{r}
mbGam <- meanBese043 %>% group_by(depth)  %>% nest() %>%
  mutate(mod = map(data, ~gam(log(nnparticles) ~ log(lb), family = gaussian(), data = .))) %>% 
  mutate(psd = map_dbl(mod, ~summary(.)$p.coeff[2]))
```

```{r}
pWBPSD <- mbGam %>% ggplot(aes(x = psd, y = depth)) + geom_path() + scale_y_reverse()  + geom_hline(yintercept = 160, color = "darkgreen") + geom_hline(yintercept =  850, color = "darkblue")
pWBPSD
```


## Fig 5 WB

bds %>% filter(profile == "stn_043", depth <= 2000) %>% ggplot(aes(x = psd_gam, xmin = psd_gam - psd_seg * 2, xmax = psd_gam + psd_seg * 2, y = depth)) + geom_path(size = 1) + scale_y_reverse() + geom_hline(yintercept = 175, color = "darkgreen") + geom_hline(yintercept = 950, color = "darkblue") + geom_ribbon(alpha = 0.2) + labs(x = "PSD slope")

All of them

```{r}
bds %>% filter(profile == "stn_043", depth <= 2000) %>% ggplot(aes(x = psd_gam, xmin = psd_gam - psd_seg * 2, xmax = psd_gam + psd_seg * 2, y = depth)) + geom_path(size = 1) + scale_y_reverse() + geom_hline(yintercept = 175, color = "darkgreen") + geom_hline(yintercept = 950, color = "darkblue") + geom_ribbon(alpha = 0.2) + labs(x = "PSD slope")
```
043 only

```{r}
bds %>% filter(profile == "stn_043", depth <= 2000, depth > 175) %>% ggplot(aes(x = small_biovolume, y = depth)) + geom_path(size = 1) + scale_y_reverse() + geom_hline(yintercept = 175, color = "darkgreen") + geom_hline(yintercept = 950, color = "darkblue") + geom_point()
```

```{r}
ubDf0 <- ToPlot %>% mutate(ubiomass = nparticles * lb ^ ag_global)
ubDf <- ubDf0 %>% group_by(time, depth) %>% summarize(ubiomass = sum(ubiomass)) %>% ungroup %>% group_by(depth)
photicBiomass <- ubDf %>% filter(depth <= 180, depth >= 160) %>% summarize(ubiomass = mean(ubiomass)) %>% pull(ubiomass)
ubDf <- ubDf %>% mutate(nbiomass = ubiomass/photicBiomass)
ubDf %>% ggplot(aes(x = nbiomass, y = depth , group = time, col = time)) + geom_path() + scale_y_reverse() + scale_x_continuous(limits = c(0,1))
```


 
```{r}
ubDf <- ToPlot %>% mutate(ubiomass = nparticles * lb ^ ag_global) %>% group_by(time, depth) %>% summarize(ubiomass = sum(ubiomass)) %>% ungroup %>% group_by(depth)  %>% summarise(ubiomass = mean(ubiomass))
photicBiomass <- ubDf %>% filter(depth <= 180, depth >= 160) %>% summarize(ubiomass = mean(ubiomass)) %>% pull(ubiomass)
ubDf <- ubDf %>% mutate(nbiomass = ubiomass/photicBiomass)
ubDf %>% ggplot(aes(x = nbiomass, y = depth)) + geom_path() + scale_y_reverse() + scale_x_continuous(limits = c(0,1)) + geom_hline(yintercept = 175, color = "darkgreen")
```


### Small particles biomass

```{r}
PubDf <- ToPlot %>% mutate(ubiomass = nparticles * lb ^ ag_global) %>% filter(lb < 0.5) %>% group_by(time, depth) %>% summarize(ubiomass = sum(ubiomass)) %>% ungroup %>% group_by(depth)  %>% summarise(ubiomass = mean(ubiomass))
photicBiomass <- PubDf %>% filter(depth <= 165, depth >= 155) %>% summarize(ubiomass = mean(ubiomass)) %>% pull(ubiomass)
PubDf <- PubDf %>% mutate(nbiomass = ubiomass/photicBiomass)
pWBS <- PubDf %>% ggplot(aes(x = nbiomass, y = depth)) + geom_path() + scale_y_reverse() + scale_x_continuous(limits = c(0,1.2)) + geom_hline(yintercept = 160, color = "darkgreen") + geom_vline(xintercept = 1, color = "gray50") + geom_vline(xintercept = 0, color = "gray50") + geom_hline(yintercept = 850, color = "darkblue") + labs( x = "Small particle mass (norm.)")
pWBS
```

```{r}
LubDf <- ToPlot %>% mutate(ubiomass = nparticles * lb ^ ag_global) %>% filter(lb >= 0.5) %>% group_by(time, depth) %>% summarize(ubiomass = sum(ubiomass)) %>% ungroup %>% group_by(depth)  %>% summarise(ubiomass = mean(ubiomass))
photicBiomass <- LubDf %>% filter(depth <= 165, depth >=155) %>% summarize(ubiomass = mean(ubiomass)) %>% pull(ubiomass)
LubDf <- LubDf %>% mutate(nbiomass = ubiomass/photicBiomass)
pWBL <- LubDf %>% ggplot(aes(x = nbiomass, y = depth)) + geom_path() + scale_y_reverse() + scale_x_continuous(limits = c(0,1)) + geom_hline(yintercept = 160, color = "darkgreen") + labs( x = "Large particle mass (norm.)") + geom_vline(xintercept = 1, color = "gray50") + geom_vline(xintercept = 0, color = "gray50") + geom_hline(yintercept = 850, color = "darkblue")
pWBL
```

For tom and danielle
```{r}
WBColorMap
pWBPSD
pWBS
pWBL
```

```{r, fig.width = 10, fig.height=3}
WBFig5 <- plot_grid(pWBPSD, pWBS,pWBL, nrow = 1, labels = c("B", "C", "D"))
WBFig5
```

```{r fig.height = 6, fig.width = 8}
WBcombined <- plot_grid(WBColorMap + theme(plot.margin = unit(c(0,3,0, 3), "cm")), WBFig5, ncol = 1, labels = c("A", ""))
WBcombined

ggsave("figures/WBModelValidation.png")
```

## P16 Flux 

```{r fig.width=6, fig.height=4}
scientific_10 <- function(x) {parse(text=gsub("e\\+*", " %*% 10^", scales::scientific_format()(x))) }
#https://stackoverflow.com/questions/10762287/how-can-i-format-axis-labels-with-exponents-with-ggplot2-and-scales
#jacob_magnitude <- function(x){expression(10^round(log10(x)))}

cb10 <- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a')
pltFlxP16 <- bds %>% filter(project == "P16") %>% #filter(DFP > 1) %>% #filter(profile %in% c("stn_043", "p16n_100")) %>%
  ggplot(aes(y = depth, x = Flux_Smooth, group = factor(time)))  + geom_point(size = 3, stroke = 1)+
  geom_path() +
  scale_y_reverse(limits = c(1000, 0))+
  scale_x_log10(limits = c(35, 150),breaks = seq(from = 20, to = 150, by = 20)) +
   scale_color_gradient2(low = "darkgreen", mid = "gray80", high = "purple", midpoint = 10) + scale_shape_manual(name = "Day of Month", values = rep(21:25, 2)) + 
  scale_fill_gradientn(name = "Hour of Day", breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) +
  
labs(x = bquote(Smoothed~Flux~(µmol~C/m^2/d)), y = "Depth (m)") +
  geom_hline(yintercept = 200, color = "darkgreen") +
  theme(axis.text.x = element_text(angle = 90, vjust = .3), legend.spacing = unit(.1, "cm"))
# 
# 
# 
# pltFlxNoLegend <- pltFlx + theme(legend.position = "none")
# pltFlxLegend <- get_legend(pltFlx)
# 
pltFlxP16
# #plotly::ggplotly(plt1)
```

```{r fig.width=6, fig.height=4}
cb10 <- c('#a6cee3','#1f78b4','#b2df8a','#33a02c','#fb9a99','#e31a1c','#fdbf6f','#ff7f00','#cab2d6','#6a3d9a')
pltDelta3P16 <- bds %>% filter(project == "P16") %>% #filter(DFP > 1) %>% #filter(profile %in% c("stn_043", "p16n_100")) %>%
  ggplot(aes(y = depth, x = pracma::nthroot(DF/DZ, 5), group = factor(time)))  + geom_point(size = 3, stroke = 1)+
  geom_path() +
  scale_y_reverse(limits = c(1000, 0))+
  scale_x_continuous(limits = c(-1, .1), breaks = seq(from = -2, to = .75, by = 0.5)) +
  #scale_x_log10() +
   scale_color_gradient2(low = "darkgreen", mid = "gray80", high = "purple", midpoint = 10) + scale_shape_manual(name = "Day of Month", values = rep(21:25, 2)) + 
  scale_fill_gradientn(name = "Hour", breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) +
  geom_vline(xintercept = 0) +
  geom_hline(yintercept = 200, color = "darkgreen")+
  labs(x = bquote((DF/DZ)^{1/5}~(µmolC/m^3/d)^{1/5}), y = "Depth (m)") + theme(legend.pos = "none")
  #labs(x = "(DF/DZ) ^ 1/5 (µmol C/m^3/d) ^ 1/5")

pltDelta3P16
#plotly::ggplotly(plt1pos)
```

```{r fig.width = 6, fig.height = 4}
osms_p16 <- bds %>% filter(project == "P16") %>%
  ggplot(aes(y = depth, x = pracma::nthroot(ospsDZ, 3), group = factor(time))) + geom_point(size = 3) + geom_path() + scale_y_reverse(limits = c(1000, 0)) +
  scale_x_continuous(limits = c(-1, 1)) +
  geom_vline(xintercept = 0) +   scale_shape_manual(name = "Day of Month", values = rep(21:25, 2)) + labs(x = "Observed - Modeled Small Particle Flux \n µmol/m^3/day") +
  scale_fill_gradientn(name = "Hour of Day", breaks = c(0, 6, 12, 18, 24), colors = c("black", "blue", "white", "orange", "black")) + geom_hline(yintercept = 175, color = "darkgreen") 
osms_p16
#ggsave("..figures/FluxSizeShift.svg"

```

```{r fig.width = 8, fig.height=8}
plot_grid(
  pltFlxP16,
  pltDelta3P16,
  osms_p16
)

ggsave("figures/P16FluxRelate.svg")
```


## EK60

```{r}
dataBinned <- read_csv("data/backscatter_table_go7.csv")
```

```{r}
dataBinned_01 <- dataBinned %>%
  mutate(timeMex = with_tz(time_bin, tzone = "US/Central") )
```

```{r}
startDay <- dataBinned_01$timeMex %>% na.omit %>% min %>% floor_date(unit = "days")
endDay <- dataBinned_01$timeMex %>% na.omit %>% max %>% ceiling_date(unit = "days")
timeBreaks <- seq(from = startDay, to = endDay, by = "12 hours")
timeLabels <- format(timeBreaks)
```


```{r}
plot18k <- dataBinned_01 %>% filter(frequency == 18000) %>% ggplot(aes(x = timeMex, y = depth_bin, fill = value)) + geom_tile() + scale_y_reverse() + scale_fill_viridis_c(limits = c(-165, -75), oob = scales::squish) +
  scale_x_datetime(breaks = timeBreaks, date_labels = "%d::%H") + labs(x = "day::hour", y = "depth (m)", fill = "backscatter (dB)") + theme_bw() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5)) + geom_hline(yintercept = 160, color = "darkgreen") +
  geom_hline(yintercept = 850, color = "darkblue")
plot18k
#ggsave("figures/stationP2_EK60_18kOnly.svg")
ggsave("figures/stationP2_EK60_18kOnly.png")
```

```{r}
plot18k + scale_y_reverse(limits = c(500, 200))
```


```{r fig.height=20, fig.width = 8}
dataBinned_01 %>% ggplot(aes(x = timeMex, y = depth_bin, fill = value)) + geom_tile() + scale_y_reverse() + scale_fill_viridis_c(limits = c(-165, -75), oob = scales::squish) +
  scale_x_datetime(breaks = timeBreaks, date_labels = "%d::%H") + labs(x = "day::hour", y = "depth (m)", fill = "backscatter (dB)") + theme_bw() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5)) +
  
  
  facet_wrap(~frequency, ncol = 1)

#ggsave("figures/stationP2_EK60_go7.svg", width = 8, height = 20)
ggsave("figures/stationP2_EK60_go7.png", width = 8, height = 20)
```